CloserLook3D
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A Closer Look at Local Aggregation Operators in Point Cloud Analysis(ECCV 2020)
A Closer Look at Local Aggregation Operators in Point Cloud Analysis
By Ze Liu, Han Hu, Yue Cao, Zheng Zhang, Xin Tong
Updates
- Oct 9, 2020: release more pytorch models for PartNet and S3DIS.
- Sep 19, 2020: add shapenetpart segmentation.
- July 3, 2020: initial release.
Introduction
This repo is the official implementation of "A Closer Look at Local Aggregation Operators in Point Cloud Analysis", which provides clean and the best (to-date) implementations for several representative operators including, Point MLP based (PointNet++-Like), Pseudo Grid based (KPConv-Like) and Adapt Weights (ContinuousConv-Like). It also includes a new family of local aggregation operators without learnable weights, named Position Pooling (PosPool), which is simpler than previous operators but performs similarly well or slightly better. Both PyTorch and TensorFlow implementations are given.
Three datasets are tested, including ModelNet, S3DIS and PartNet. Our implementations all achieve (or are close to) the state-of-the-art accuracy on these benchmarks by proper configurations of each operator type. In particular, one settings achieves 53.8 part category mean IoU on PartNet test set, which outperforms previous best implementations by 7.4 mIoU.
Citation
@article{liu2020closerlook3d,
title={A Closer Look at Local Aggregation Operators in Point Cloud Analysis},
author={Liu, Ze and Hu, Han and Cao, Yue and Zhang, Zheng and Tong, Xin},
journal={ECCV},
year={2020}
}
Main Results
ModelNet40
| Method | Acc | Tensorflow Model | Pytorch Model |
|---|---|---|---|
| Point-wise MLP | 92.8 | Google / Baidu(wquw) | Google / Baidu(fj13) |
| Pseudo Grid | 93.0 | Google / Baidu(lvw4) | Google / Baidu(gmh5) |
| Adapt Weights | 93.0 | Google / Baidu(6zrg) | Google / Baidu(bbus) |
| PosPool | 92.9 | Google / Baidu(pkzd) | Google / Baidu(wuuv) |
| PosPool* | 93.2 | Google / Baidu(mjb1) | Google / Baidu(qcc6) |
S3DIS
| Method | mIoU | Tensorflow Model | Pytorch Model |
|---|---|---|---|
| Point-wise MLP | 66.2 | Google / Baidu(4mhy) | Google / Baidu(53as) |
| Pseudo Grid | 65.9 | Google / Baidu(06ta) | Google / Baidu(8skn) |
| Adapt Weights | 66.5 | Google / Baidu(7w43) | Google / Baidu(b7zv) |
| PosPool | 66.5 | Google / Baidu(gqqe) | Google / Baidu(z752) |
| PosPool* | 66.7 | Google / Baidu(qtkw) | Google / Baidu(r96f) |
PartNet
| Method | mIoU (val) | mIoU (test) | Tensorflow Model | Pytorch Model |
|---|---|---|---|---|
| Point-wise MLP | 48.1 | 51.2 | Google / Baidu(zw15) | Google / Baidu(wxff) |
| Pseudo Grid | 50.8 | 53.0 | Google / Baidu(0mtr) | Google / Baidu(n6b7) |
| Adapt Weights | 50.1 | 53.5 | Google / Baidu(551l) | Google / Baidu(pc22) |
| PosPool | 50.0 | 53.4 | Google / Baidu(rb4x) | Google / Baidu(3qv5) |
| PosPool* | 50.6 | 53.8 | Google / Baidu(2ts3) | Google / Baidu(czyq) |
ShapeNetPart
| Method | mIoU | msIoU | Acc | Pytorch Model |
|---|---|---|---|---|
| Point-wise MLP | 85.7 | 84.1 | 94.5 | Google / Baidu(mi2m) |
| Pseudo Grid | 86.0 | 84.3 | 94.6 | Google / Baidu(wde6) |
| Adapt Weights | 85.9 | 84.5 | 94.6 | Google / Baidu(dy1k) |
| PosPool | 85.9 | 84.6 | 94.6 | Google / Baidu(r2tr) |
| PosPool* | 86.2 | 84.8 | 94.8 | Google / Baidu(27ie) |
Notes:
- Overall accuracy for ModelNet40, mean IoU for S3DIS with Area-5, mean part-category IoU for PartNet are reported.
Point-wise MLPdenotesPointNet++-likeoperators.Pseudo GriddenotesKPConv-likeoperators.Adapt WeightsdenotesContinuousConv-likeoperators.PosPoolis a new parameter-free operator.PosPool*denotes the sin/cos embedding variant ofPosPool(see description in the paper).
Install
- For
tensorflowusers, please refer to README.md for more detailed instructions. Our main experiments are conducted using this code base. - For
pytorchusers, please refer to README.md for more detailed instructions.
Acknowledgements
Our tensorflow codes borrowed a lot from KPCONV.
License
The code is released under MIT License (see LICENSE file for details).
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